| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Powder Materials Processing and Fundamental Understanding
|
| Presentation Title |
A Bayesian Approach to the Eagar-Tsai Model for Melt Pool Geometry Predictions |
| Author(s) |
Brendan J. Whalen, Prasanna Balachandran |
| On-Site Speaker (Planned) |
Brendan J. Whalen |
| Abstract Scope |
The objective of this work is to improve melt pool geometry predictions and quantify uncertainties in the laser powder bed fusion (L-PBF) process using an adapted version of the Eagar-Tsai (E-T) model. Temperature-dependent properties of the material and powder conditions are incorporated into the conventional E-T model. Bayesian inference is employed to predict distributions for the E-T model input parameters of laser absorptivity and powder bed porosity by incorporating experimental results. Although conventionally treated as constant values in simulations and modelling, our Bayesian analysis results for 316L stainless steel (316L SS) and Ti-6Al-4V (Ti64) suggest that the absorptivity and powder bed porosity are influenced by laser power and scanning speed. Printability maps for 316L SS and Ti64 are created from the Bayesian fit adapted E-T model, which now predicts the lack of fusion and keyhole regions that were typically out of the scope for the conventional E-T model. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Computational Materials Science & Engineering, Powder Materials |